Large Language Model
6445dd88ebb9a6a3afa0b126ad87fe41-Supplemental-Conference.pdf
Traditional programming languages result in some unique challenges for language models.
Parsel: Algorithmic Reasoning with Language Models by Composing Decompositions
We introduce Parsel, a framework enabling automatic implementation and validation of complex algorithms with code LLMs.
A Manually Annotated Dataset for Instruction-Guided Image Editing
Text-guided image editing is widely needed in daily life, ranging from personal use to professional applications such as Photoshop. However, existing methods are either zero-shot or trained on an automatically synthesized dataset, which contains a high volume of noise. Thus, they still require lots of manual tuning to produce desirable outcomes in practice.
Assessing Causal Reasoning in Language Models
The ability to perform causal reasoning is widely considered a core feature of intelligence.
LoRA: A Logical Reasoning Augmented Dataset for Visual Question Answering
The capacity to reason logically is a hallmark of human cognition.
Effective Human-AI Teams via Learned Natural Language Rules and Onboarding
People are relying on AI agents to assist them with various tasks.
ForkMerge: Mitigating Negative Transfer in Auxiliary-Task Learning
Even in large language models, negative transfer problems may still exist.